Auto-Generated EMS Drivers for New Device Integration
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Solution Overview
Problem
Existing energy management systems face inefficiencies when integrating new energy management devices, requiring significant coding efforts and resource allocation for interface development, and handling high data volumes, leading to excessive processing and memory usage.
Innovation Solution
An energy management system that automatically generates drivers to interface with new energy management devices, using a driver generator to create executable code that maps data points to predefined variables, allowing existing software applications to operate with new devices without redeployment, and optimizing data handling by grouping data points into blocks for efficient transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional interface development methods are used for new energy management devices, then the system can integrate new devices, but significant coding efforts and resource allocation are required
Solution Approach 1:
The system performs self-configuration by automatically generating driver code and data point mappings when a new energy management device is connected. The driver generator component creates the necessary interface code based on device information, eliminating the need for manual coding efforts and enabling seamless integration of new devices
Solution Approach 2:
The system pre-generates driver templates and data point configurations before actual device integration. By having the driver generator create standardized interface code and mappings in advance, the system prepares all necessary components for quick device integration without requiring significant coding effort at the time of connection
2Reliability
If high data volumes are handled by the energy management system, then comprehensive monitoring is achieved, but excessive processing and memory usage occur
Solution Approach 1:
The system segments data points into structured groups and categories, organizing them hierarchically with parent-child relationships. This segmentation allows the system to process and monitor data in manageable units rather than handling all data points as a single large volume, reducing processing overhead while maintaining comprehensive monitoring capability
Solution Approach 2:
The system dynamically changes data acquisition parameters based on device type and operational state. By adjusting sampling rates, data retention periods, and monitoring frequencies according to specific device requirements, the system optimizes processing resource usage while ensuring adequate monitoring coverage for all energy management devices
3Ease of operation
If data points are transmitted individually, then precise data control is maintained, but excessive network traffic is generated
Solution Approach 1:
The system merges multiple individual data point transmissions into consolidated data blocks or batches. By grouping related data points and transmitting them together rather than individually, the system significantly reduces network traffic volume while maintaining precise control over each data point through the structured data model and selective access mechanisms
Data Source
AI summary
An energy management system can include one or more memory devices. The one or more memory devices can store instructions thereon, that, when executed by one or more processors, cause the one or more processors to receive a file including data points of an energy management device coupled to the one or more processors through an interface. The instructions can cause the one or more processors to generate a driver based on the data points of the file. The instructions can cause the one or more processors to execute the driver to read at least one of the data points or write to at least one of the data points via the interface.